{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd            # as 是对包或模块重命名\n",
    "# import pandas.io.data as web   # 导入包和模块，模块可能随着版本的不同会发生变化ImportError: The pandas.io.data module is moved to a separate package (pandas-datareader). \n",
    "# After installing the pandas-datareader package (https://github.com/pydata/pandas-datareader),\n",
    "#  you can change the import ``from pandas.io import data, wb`` to ``from pandas_datareader import data, wb``.\n",
    "\n",
    "import pandas_datareader.data as web \n",
    "import datetime\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader.data as web   \n",
    "import datetime\n",
    "start = datetime.datetime(2016,1,1)\n",
    "end = datetime.date.today()\n",
    "apple = web.DataReader(\"AAPL\", \"yahoo\", start, end)\n",
    " \n",
    "#  web.DataReader(\"AAPL\", \"yahoo\", start, end)会出现下处错误\n",
    "# Let's get Apple stock data; Apple's ticker symbol is AAPL\n",
    "# First argument is the series we want, second is the source (\"yahoo\" for Yahoo! Finance), third is the start date, fourth is the end date\n",
    "\n",
    "# if yahoo forbidden using \"pip install fix_yahoo_finance\"\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " \n",
    "import pandas_datareader.data as web\n",
    "import datetime\n",
    "import yfinance as yf\n",
    "yf.pdr_override()\n",
    " \n",
    "start=datetime.datetime(2006, 1, 1)\n",
    "end=datetime.datetime(2012, 1, 1)\n",
    "apple=web.get_data_yahoo('AAPL',start,end)\n",
    "apple\n",
    "\t\n",
    "apple.head()\n",
    " \n",
    "\n",
    "# start=datetime.datetime(2017, 1, 1)\n",
    "# end=datetime.datetime.today()\n",
    "# apple=web.get_data_yahoo('AAPL',start,end)\n",
    "# apple\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt   # Import matplotlib\n",
    "# This line is necessary for the plot to appear in a Jupyter notebook\n",
    "%matplotlib inline\n",
    " \n",
    "# Control the default size of figures in this Jupyter notebook\n",
    "%pylab inline\n",
    " \n",
    "pylab.rcParams['figure.figsize'] = (15, 9)   # Change the size of plots\n",
    " \n",
    "apple[\"Adj Close\"].plot(grid = True) # Plot the adjusted closing price of AAPL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\vnstudio\\lib\\site-packages\\mplfinance\\_arg_validators.py:45: UserWarning: \n",
      "\n",
      " ================================================================= \n",
      "\n",
      "   WARNING: YOU ARE PLOTTING SO MUCH DATA THAT IT MAY NOT BE\n",
      "            POSSIBLE TO SEE DETAILS (Candles, Ohlc-Bars, Etc.)\n",
      "   For more information see:\n",
      "   - https://github.com/matplotlib/mplfinance/wiki/Plotting-Too-Much-Data\n",
      "   \n",
      "   TO SILENCE THIS WARNING, set `type='line'` in `mpf.plot()`\n",
      "   OR set kwarg `warn_too_much_data=N` where N is an integer \n",
      "   LARGER than the number of data points you want to plot.\n",
      "\n",
      " ================================================================ \n",
      "  category=UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "add_plot = mpf.make_addplot(apple[['High',  'Low']])\n",
    "\n",
    "# mpf.plot(apple, type='candle', addplot=add_plot)\n",
    "mpf.plot(apple, type='candle',addplot=add_plot, mav=(2, 5, 10), volume=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'mplfinance' has no attribute 'candlestick_ohlc'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_16084/3073094688.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots_adjust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbottom\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[0mmpf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcandlestick_ohlc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata_mat\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolordown\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'#53c156'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolorup\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'#ff1717'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     35\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;31m#将x轴的浮点数格式化成日期小时分钟\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'mplfinance' has no attribute 'candlestick_ohlc'"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as dates\n",
    "import mplfinance as mpf\n",
    "from matplotlib.ticker import Formatter\n",
    "import numpy as np\n",
    " \n",
    " \n",
    "dfcvs = DataFrame([\n",
    "    [\"2018/09/17-21:34\", 3646, 3650,3644,3650],\n",
    "    [\"2018/09/17-21:35\", 3650, 3650,3648,3648],\n",
    "    [\"2018/09/17-21:36\", 3650, 3650,3648,3650],\n",
    "    [\"2018/09/17-21:37\", 3652, 3654,3648,3652]\n",
    "])\n",
    " \n",
    "dfcvs.columns = ['时间','开盘','最高','最低','收盘']\n",
    "dfcvs['时间']=pd.to_datetime(dfcvs['时间'],format=\"%Y/%m/%d-%H:%M\")\n",
    " \n",
    "#matplotlib的date2num将日期转换为浮点数，整数部分区分日期，小数区分小时和分钟\n",
    "#因为小数太小了，需要将小时和分钟变成整数，需要乘以24（小时）×60（分钟）=1440，这样小时和分钟也能成为整数\n",
    "#这样就可以一分钟就占一个位置\n",
    " \n",
    " \n",
    " \n",
    "dfcvs['时间']=dfcvs['时间'].apply(lambda x:dates.date2num(x)*1440)\n",
    "# data_mat=dfcvs.as_matrix()\n",
    "data_mat=dfcvs.iloc[:,:].values\n",
    "# train_np = train_df.iloc[:,:].values\n",
    "    \n",
    "fig,ax=plt.subplots(figsize=(1200/72,480/72))\n",
    " \n",
    "fig.subplots_adjust(bottom=0.1)   \n",
    "mpf.candlestick_ohlc(ax,data_mat,colordown='#53c156', colorup='#ff1717',width=0.2,alpha=1)\n",
    " \n",
    "#将x轴的浮点数格式化成日期小时分钟\n",
    "#默认的x轴格式化是日期被dates.date2num之后的浮点数，因为在上面乘以了1440，所以默认是错误的\n",
    "#只能自己将浮点数格式化为日期时间分钟\n",
    "#参考https://matplotlib.org/examples/pylab_examples/date_index_formatter.html\n",
    "class MyFormatter(Formatter):\n",
    "            def __init__(self, dates, fmt='%Y%m%d %H:%M'):\n",
    "                self.dates = dates\n",
    "                self.fmt = fmt\n",
    "    \n",
    "            def __call__(self, x, pos=0):\n",
    "                'Return the label for time x at position pos'\n",
    "                ind = int(np.round(x))\n",
    "                #ind就是x轴的刻度数值，不是日期的下标\n",
    " \n",
    "                return dates.num2date( ind/1440).strftime(self.fmt)\n",
    "        \n",
    "formatter = MyFormatter(data_mat[:,0])\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    " \n",
    "for label in ax.get_xticklabels():\n",
    "            label.set_rotation(90)\n",
    "            label.set_horizontalalignment('right')\n",
    "           \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'matplotlib.finance'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_16084/127674631.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdates\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDateFormatter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mWeekdayLocator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mDayLocator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMONDAY\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfinance\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcandlestick_ohlc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpandas_candlestick_ohlc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstick\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"day\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0motherseries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib.finance'"
     ]
    }
   ],
   "source": [
    "from matplotlib.dates import DateFormatter, WeekdayLocator,\\\n",
    "    DayLocator, MONDAY\n",
    "from matplotlib.finance import candlestick_ohlc\n",
    "from mpl_finance import quotes_historical_yahoo\n",
    "def pandas_candlestick_ohlc(dat, stick = \"day\", otherseries = None):\n",
    "    \"\"\"\n",
    "    :param dat: pandas DataFrame object with datetime64 index, and float columns \"Open\", \"High\", \"Low\", and \"Close\", likely created via DataReader from \"yahoo\"\n",
    "    :param stick: A string or number indicating the period of time covered by a single candlestick. Valid string inputs include \"day\", \"week\", \"month\", and \"year\", (\"day\" default), and any numeric input indicates the number of trading days included in a period\n",
    "    :param otherseries: An iterable that will be coerced into a list, containing the columns of dat that hold other series to be plotted as lines\n",
    " \n",
    "    This will show a Japanese candlestick plot for stock data stored in dat, also plotting other series if passed.\n",
    "    \"\"\"\n",
    "    mondays = WeekdayLocator(MONDAY)        # major ticks on the mondays\n",
    "    alldays = DayLocator()              # minor ticks on the days\n",
    "    dayFormatter = DateFormatter('%d')      # e.g., 12\n",
    " \n",
    "    # Create a new DataFrame which includes OHLC data for each period specified by stick input\n",
    "    transdat = dat.loc[:,[\"Open\", \"High\", \"Low\", \"Close\"]]\n",
    "    if (type(stick) == str):\n",
    "        if stick == \"day\":\n",
    "            plotdat = transdat\n",
    "            stick = 1 # Used for plotting\n",
    "        elif stick in [\"week\", \"month\", \"year\"]:\n",
    "            if stick == \"week\":\n",
    "                transdat[\"week\"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[1]) # Identify weeks\n",
    "            elif stick == \"month\":\n",
    "                transdat[\"month\"] = pd.to_datetime(transdat.index).map(lambda x: x.month) # Identify months\n",
    "            transdat[\"year\"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[0]) # Identify years\n",
    "            grouped = transdat.groupby(list(set([\"year\",stick]))) # Group by year and other appropriate variable\n",
    "            plotdat = pd.DataFrame({\"Open\": [], \"High\": [], \"Low\": [], \"Close\": []}) # Create empty data frame containing what will be plotted\n",
    "            for name, group in grouped:\n",
    "                plotdat = plotdat.append(pd.DataFrame({\"Open\": group.iloc[0,0],\n",
    "                                            \"High\": max(group.High),\n",
    "                                            \"Low\": min(group.Low),\n",
    "                                            \"Close\": group.iloc[-1,3]},\n",
    "                                           index = [group.index[0]]))\n",
    "            if stick == \"week\": stick = 5\n",
    "            elif stick == \"month\": stick = 30\n",
    "            elif stick == \"year\": stick = 365\n",
    " \n",
    "    elif (type(stick) == int and stick >= 1):\n",
    "        transdat[\"stick\"] = [np.floor(i / stick) for i in range(len(transdat.index))]\n",
    "        grouped = transdat.groupby(\"stick\")\n",
    "        plotdat = pd.DataFrame({\"Open\": [], \"High\": [], \"Low\": [], \"Close\": []}) # Create empty data frame containing what will be plotted\n",
    "        for name, group in grouped:\n",
    "            plotdat = plotdat.append(pd.DataFrame({\"Open\": group.iloc[0,0],\n",
    "                                        \"High\": max(group.High),\n",
    "                                        \"Low\": min(group.Low),\n",
    "                                        \"Close\": group.iloc[-1,3]},\n",
    "                                       index = [group.index[0]]))\n",
    " \n",
    "    else:\n",
    "        raise ValueError('Valid inputs to argument \"stick\" include the strings \"day\", \"week\", \"month\", \"year\", or a positive integer')\n",
    " \n",
    " \n",
    "    # Set plot parameters, including the axis object ax used for plotting\n",
    "    fig, ax = plt.subplots()\n",
    "    fig.subplots_adjust(bottom=0.2)\n",
    "    if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):\n",
    "        weekFormatter = DateFormatter('%b %d')  # e.g., Jan 12\n",
    "        ax.xaxis.set_major_locator(mondays)\n",
    "        ax.xaxis.set_minor_locator(alldays)\n",
    "    else:\n",
    "        weekFormatter = DateFormatter('%b %d, %Y')\n",
    "    ax.xaxis.set_major_formatter(weekFormatter)\n",
    " \n",
    "    ax.grid(True)\n",
    " \n",
    "    # Create the candelstick chart\n",
    "    candlestick_ohlc(ax, list(zip(list(date2num(plotdat.index.tolist())), plotdat[\"Open\"].tolist(), plotdat[\"High\"].tolist(),\n",
    "                      plotdat[\"Low\"].tolist(), plotdat[\"Close\"].tolist())),\n",
    "                      colorup = \"black\", colordown = \"red\", width = stick * .4)\n",
    " \n",
    "    # Plot other series (such as moving averages) as lines\n",
    "    if otherseries != None:\n",
    "        if type(otherseries) != list:\n",
    "            otherseries = [otherseries]\n",
    "        dat.loc[:,otherseries].plot(ax = ax, lw = 1.3, grid = True)\n",
    " \n",
    "    ax.xaxis_date()\n",
    "    ax.autoscale_view()\n",
    "    plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')\n",
    " \n",
    "    plt.show()\n",
    " \n",
    "pandas_candlestick_ohlc(apple)"
   ]
  }
 ],
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